Galectin-3 and Soluble CD146 Identify Cardiorenal Injuries in Severe Burn Patients: A Biomarker-Based Approach
Bibliographic record
Abstract
INTRODUCTION: Acute kidney injury (AKI) and myocardial injury (MI) are severe conditions in patients with severe burn injury, and combination of both is even worst and is called the cardiorenal syndrome (CRS). Identifying a distinct cardiorenal phenotype could significantly enhance the management of these patients. Galectin-3 (Gal3) and soluble CD146 (sCD146) are biomarkers for renal and cardiac injuries. This study aims to assess the occurrence and reliability of these biomarkers in recognizing CRS in individuals who have been severely burn. METHODS: This study is a single-center prospective proof-of-concept study involving patients with severe burn injuries. Plasma samples for Gal3 and sCD146 measurements were collected daily during the initial 7 days following admission. CRS was defined after 24 h of admission by the association of AKI stage 1 or more (KDIGO definition) and MI defined on high sensitive troponin (hsTnT) (variation >20% baseline value or absolute value >40 ng/mL). RESULTS: Forty patients met the inclusion criteria and were included in this study. Thirty-eight patients had CRS. The pooled values of Gal3 or combination of Gal3 and sCD146 values following 7 days after admission were associated with CRS with an odds ratio (OR) of 1.145 (95% CI: 1.081-1.211), p < 0.001, and 1.147 (95% CI: 1.085-1.212), p < 0.001, respectively. Gal3 values at admission (D0) had a predictive performance for CRS with an AUC of 0.78 (95% CI: 0.63-0.93), and this performance improved when using the combination of Gal3 and sCD146 values at admission (D0), with an AUC of 0.81 (95% CI: 0.66-0.96). Gal3 levels during the first 7 days were associated with patients experiencing AKI and no MI, with an OR of 1.129 (95% CI: 1.065-1.195), p < 0.001, and MI without AKI with an OR of 1.095 (95% CI: 1.037-1.167), p < 0.001. sCD146 alone was not associated with AKI without MI or MI without AKI and was poorly associated with CRS. CONCLUSION: In severely burned patients, CRS is a frequent and severe condition. Gal3 values during the first 7 days following admission were associated with CRS. The use of sCD146 with Gal3 improved prediction performance for CRS identification. The use of such biomarkers to identify CRS is important and needs to be confirmed in other studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".